As financial crime grows more sophisticated, organisations are turning to cutting-edge technology to stay ahead of perpetrators. Leveraging advancements like artificial intelligence (AI), machine learning (ML), automation and data analytics within Anti-Money Laundering (AML) and Counter-Terrorism Financing (CTF) frameworks has become essential in today’s complex risk landscape. This integration is critical, especially as organisations confront rising costs and heightened expectations for effective financial crime risk management.
Yet, our experience at PwC with both local and global organisations, reveals that many face foundational roadblocks that must be cleared for these solutions to fully deliver on their promise. Issues such as disparate data sources, outdated legacy systems and integration challenges often hold organisations back. Simplifying and modernising these areas is necessary, as well as fostering a cohesive data and technology strategy.
In this article, we explore how organisations can harness emerging technologies to transform their financial crime prevention capabilities. Drawing from our latest research and experience, we delve into five critical areas:
AI-driven obligation and compliance gap identification
Many organisations are currently navigating or preparing to navigate regulatory changes in AML/CTF, anti-bribery and corruption, sanctions, and the new scams prevention framework. AI can help streamline these changes, translating complex regulations into specific obligations and assessing their relevance to the organisation. From there, AI models can support the augmentation of different use cases such as comparing obligations to an organisation’s existing artefacts (e.g. policies, standards, guidelines or control libraries) to identify potential compliance gaps and make recommendations. This streamlined approach not only speeds up compliance processes but also strengthens it, making organisations more resilient to financial crime risks and regulatory change.
Automated controls assurance
Controls assurance is vital for managing risks, ensuring compliance, safeguarding financial integrity and building stakeholder trust. We are seeing automation used to enhance the assurance of internal controls and reduce the volume of manual testing, particularly, in relation to AML/CTF obligations. Continuously running automated controls monitoring can identify process breakdowns in near real-time, allowing for swift responses that traditional periodic methods can’t match. The result? Faster, more efficient issue resolution that keeps potential risks in check and a more effective approach to investment prioritisation and strategic decision making.
Centralised financial crime data platforms and data mesh
High-quality, accessible data is at the heart of effective financial crime management. A centralised financial crime data platform empowers analytics teams with access to curated, historical customer account and transaction data, along with enriched unstructured datasets. This then supports advanced analytical models, high-performance querying, regulatory reporting, investigations and intelligence sharing, becoming the source of truth for decision-making and risk assessments. Typically implemented using scalable cloud-based data ‘lakehouses’ (that merge the flexibility, scalability, and cost-efficiency of data lakes with the performance and reliability of data warehouses) these platforms should integrate with trusted third-party data sources and offer enriched data for broader organisational use. By incorporating a data mesh approach, organisations can treat data as a reusable product across departments, enhancing fraud and scam prevention and detection.
Intelligence-led detection and investigations
AI-powered intelligence is a game-changer for investigations, enabling analysts to process large datasets and summarise findings quickly. With the support of AI ‘co-pilots’ investigators can efficiently identify and investigate suspicious activities. A notable use case is using blockchain AI to monitor and analyse cryptocurrency transactions. It’s particularly effective in preventing and detecting cryptocurrency scams through the analysis of data from disparate sources to identify anomalous customer activity. This technology brings unprecedented speed and accuracy to detection strategies and investigations, allowing teams to focus on managing and reducing financial crime risks.
Perpetual Know Your Customer (pKYC)
Lastly, advancements in technology are empowering more organisations than ever to shift from traditional, periodic KYC approaches to more dynamic methods. While pKYC isn’t an entirely new concept, recent progress in regulatory technology solutions provided by third-party vendors (RegTech), automation and greater on-demand access to enriched external data is bringing us closer to making it a practical reality.
As the banking industry moves toward a standardised approach to pKYC, organisations need to consider a few key factors. These include:
- Enabling straight through processing to automate the majority of alerts.
- Managing the initial impact on operational teams during the early stages of implementation.
- Continual validation of pKYC approaches and models against regulatory requirements and industry standards.
- Ensuring customer data is accurately managed, and protected, to support the long-term sustainability of these solutions.
- Realising the cost efficiencies that pKYC can bring over time.
Final thoughts…
The fight against financial crime is an ongoing battle demanding continuous innovation and adaptation. However, it's essential to tackle those common foundational challenges before implementing big-bang solutions across the organisation. By addressing these basics first, organisations can fully leverage these new technologies, strengthening their resilience in an ever-evolving financial crime landscape.
